Comparison of Measurements of Atmospheric Wet Delay by Radiosonde, Water Vapor Radiometer, GPS, and VLBI

Niell, A. E.; Coster, A. J.; Solheim, Fredrick; Mendes, V. B.; Toor, P. C.; Langley, Richard B.; Upham, Carolyn A.

doi:10.1175/1520-0426(2001)018<0830:comoaw>2.0.co;2

Cited by 251 publications

(196 citation statements)

References 37 publications

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“…One of the most commonly used methods to obtain the ZHD is using the Saastamoinen formula, which is a function of surface pressure (Saastamoinen, 1972;Elgered et al, 1991;Niell et al, 2001). Davis et al (1985) pointed out that the uncertainty in the ZHD obtained from the Saatamoinen formula was 0.5 mm, if uncertainties in the physical constants and the calculation of the mean value of gravity were taken into consideration.…”

Section: Computation Of Zhd and Iwvmentioning

confidence: 99%

“…Accurate knowledge of water vapor is not only vital for weather forecasting but also an important independent data source for global climate studies. For the last decade, the Global Navigation Satellite System (GNSS) has been used as an emerging and robust tool for remotely sensing integrated water vapor (IWV) for the monitoring of the real-time IWV variations in the atmosphere (Schneider et al, 2010;Rohm et al, 2014;Zhang et al, 2015;Li et al, 2014Li et al, , 2015Guerova et al, 2016) or the studies of climate (Nilsson and Elgered, 2008;Jin and Luo, 2009;Vonder Haar et al, 2012;Ning and Elgered, 2012) due to its 24 h availability, high accuracy, global coverage, high resolution and low cost. The atmospheric parameter directly estimated from GNSS measurements is the GNSS signal's tropospheric zenith total delay (ZTD) which can be effectively divided into the zenith hydrostatic delay (ZHD) and the zenith wet delay (ZWD).…”

Section: Introductionmentioning

confidence: 99%

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Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

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Abstract. Surface pressure is a necessary meteorological variable for the accurate determination of integrated water vapor (IWV) using Global Navigation Satellite System (GNSS). The lack of pressure observations is a big issue for the conversion of historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either a blind model (e.g., Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g., ERAInterim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations at four epochs (00:00, 06:00, 12:00 and 18:00 UTC) each day for the period 2000-2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band between −30 and 30 • and the average value of 6 h root-mean-square errors (RMSEs) across all the stations in this region is 2.5 hPa. Correspondingly, an error of 5.8 mm and 0.9 kg m −2 in its resultant zenith hydrostatic delay (ZHD) and IWV is expected. However, for the stations located in the mid-latitude bands between −30 and −60 • and between 30 and 60 • , the mean value of the RMSEs is 7.3 hPa, and for the stations located in the high-latitude bands from −60 to −90 • and from 60 to 90 • , the mean value of the RMSEs is 9.9 hPa. The mean of the RMSEs of the ERAInterim-derived pressure across at the selected 100 stations is 0.9 hPa, which will lead to an equivalent error of 2.1 mm and 0.3 kg m −2 in the ZHD and IWV, respectively, determined from this ERA-Interim-derived pressure. Results also show that the monthly IWV determined using pressure from ERAInterim has a good accuracy − with a relative error of better than 3 % on a global scale; thus, the monthly IWV resulting from ERA-Interim-derived pressure has the potential to be used for climate studies, whilst the monthly IWV resulting from GPT2w-derived pressure has a relative error of 6.7 % in the mid-latitude regions and even reaches 20.8 % in the highlatitude regions. The comparison between GPT2w and seasonal models of pressure-ZHD derived from ERA-Interim and pressure observations indicates that GPT2w captures the seasonal variations in pressure-ZHD very well.

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Section: Computation Of Zhd and Iwvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

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“…The accuracy of IWV has been assessed using intercomparisons with radiosondes, microwave radiometers, sun photometers, lidars and Very Long Baseline Interferometer (e.g. Niell et al, 2001;Bock et al, 2005). The general agreement found between these techniques is about 1-2 kg m −2 .…”

Section: Description Of the Datasetmentioning

confidence: 99%

Water vapour variability induced by urban/rural surface heterogeneities during convective conditions

Champollion

Drobinski

Haeffelin

et al. 2009

Quart J Royal Meteoro Soc

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Scientific interest in urban meteorology has increased because highly populated areas experience high vulnerability to pollution or heavy rain. However, compared to urban air quality or urban heat island (UHI) processes, the urban water vapour cycle is poorly understood because it has been investigated less due to the lack of upper-air measurements and the high sensitivity of surface measurements to local heterogeneities. In this paper, surface measurements of wind, temperature, pressure and humidity, as well as integrated water vapour (IWV) from GPS and MODIS and numerical simulations, have been used to investigate the urban cycle of water vapour in May and June 2004 during the VAPIC field experiment in the Paris area.The surface data show the typical characteristics of an urban area with the absence of water vapour sources and a UHI of about 6 • C at night. The urban IWV distribution differs completely, with an urban IWV excess on average between 1600 and 0600 UTC (with a maximum of about 1.5 kg m −2 at 0600 and 1700 UTC). No IWV difference between the urban and rural areas is found in the middle of the day. The numerical simulations reproduce accurately the urban IWV anomaly.Shallow surface wind convergence associated with the UHI during nighttime provides moisture from the rural areas. Urban areas are therefore under wind convergence for most of the time. The rural water vapour sources and the depth of the convergence control the amplitude of the urban IWV excess. At about 1200 UTC, entrainment at the top of the urban boundary layer is the key process that inhibits the urban IWV excess observed at night.

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“…ZWD is, thus, converted into IWV (Integrated Water Vapor), using simply surface temperature and empirical formulas (Bevis et al, 1992;Emardson and Derks, 1999). The accuracy in GPS, IWV has been assessed by a number of authors, using intercomparisons with radiosondes, microwave radiometers, sun photometers, lidars, and very long interferometry baseline (Foelsche and Kirchengast, 2001;Niell et al, 2001;Bock et al, 2004). The agreement between these techniques is about 1-2 kg m −2 for typical values of IWV between 5 and 30 kg m −2 .…”

Section: Radiosonde Sensors and Gps Receiversmentioning

confidence: 99%

Water vapor observations up to the lower stratosphere through the Raman lidar during the Maïdo Lidar Calibration Campaign

et al. 2015

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Abstract.A new lidar system devoted to tropospheric and lower stratospheric water vapor measurements has been installed at the Maïdo altitude station facility of Réunion island, in the southern subtropics.To evaluate the performances and the capabilities of the new system with a particular focus on UTLS (Upper Troposphere Lower Stratosphere) measurements, the Maïdo Lidar Calibration Campaign (MALICCA) was performed in April 2013.Varying the characteristics of the transmitter and the receiver components, different system configuration scenarios were tested and possible parasite signals (fluorescent contamination, rejection) were investigated. A hybrid calibration methodology has been set up and validated to insure optimal lidar calibration stability with time. In particular, the receiver transmittance is monitored through the calibration lamp method that, at the moment, can detect transmittance variations greater than 10-15 %. Calibration coefficients are then calculated through the hourly values of IWV (Integrated Water Vapor) provided by the co-located GPS. The comparison between the constants derived by GPS and Vaisala RS92 radiosondes launched at Maïdo during MALICCA, points out an acceptable agreement in terms of accuracy of the mean calibration value (with a difference of approximately 2-3 %), but a significant difference in terms of variability (14 % vs. 7-9 %, for GPS and RS92 calibration procedures, respectively).We obtained a relatively good agreement between the lidar measurements and 15 co-located and simultaneous RS92 radiosondes. A relative difference below 10 % is measured in the low and middle troposphere (2-10 km). The upper troposphere (up to 15 km) is characterized by a larger spread (approximately 20 %), because of the increasing distance between the two sensors.To measure water vapor in the UTLS region, nighttime and monthly water vapor profiles are presented and compared. The good agreement between the lidar monthly profile and the mean WVMR profile measured by satellite MLS (Microwave Limb Sounder) has been used as a quality control procedure of the lidar product, attesting the absence of significant wet biases and validating the calibration procedure.Due to its performance and location, the MAIDO H 2 O lidar will become a reference instrument in the southern subtropics, insuring the long-term survey of the vertical distribution of water vapor. Furthermore, this system allows the investigation of several scientific themes, such as stratospheretroposphere exchange, tropospheric dynamics in the subtropics, and links between cirrus clouds and water vapor.

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Comparison of Measurements of Atmospheric Wet Delay by Radiosonde, Water Vapor Radiometer, GPS, and VLBI

Cited by 251 publications

References 37 publications

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Water vapour variability induced by urban/rural surface heterogeneities during convective conditions

Water vapor observations up to the lower stratosphere through the Raman lidar during the Maïdo Lidar Calibration Campaign

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